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Inductive Reasoning

Chapter Outline

Outline/Summary of Objectives in Chapter Eleven: Inductive Reasoning

Following are the main learning objectives from the chapter.

Students should become proficient in methods for evaluating inductive arguments. To this end, students should be familiar with the standards of evaluation for the following inductive argument patterns:

(See 306-317)

Inductive Generalizations (pgs. 306-317)

A. In understanding inductive generalizations, you should . . .

1. be able to identify the sample population and the population as a whole (i.e. the population that the generalization is about) in an inductive generalization.

2. understand that a good inductive argument should reach a conclusion that is appropriate to the evidence offered in the premises.

a. A more moderate conclusion makes the inference stronger.

b. An overstated conclusion makes the inference weaker.

B. In evaluating inductive generalizations you should ask the following questions:

1. Are the premises true?

2. Is the sample population large enough?

3. Is the sample population representative of the population as a whole?

a. You should understand that a representative sample is similar to the population as a whole in all relevant respects.

C. In evaluating generalizations that are generated through opinion polls, you should . . .

1. understand that opinion polls operate under the same basic standards as other inductive generalizations insofar as the sample must be large enough and representative of the population as a whole;

a. The size of the sample should be large enough to reach an acceptable margin of error.

b. The sample is best generated randomly (where each member of the population has an equal chance of being selected) so as to avoid bias.

2. grasp the concepts of level of certainty and margin of error;

3. recognize the weaknesses in self-selecting samples;

4. understand how the tendency of people to respond to polls dishonestly, and the tendency of agencies with vested interests to ask slanted questions, can bias a poll sample;

5. recognize the merits of a double-blind poll for generating objective results.

(See 317-321)

Statistical Arguments (pgs. 317-321)

A. In evaluating statistical arguments, students should . . .

1. understand the distinction between inductive strength and statistical reliability in statistical arguments;

2. understand how the specificity of the reference class in a statistical argument can impact the strength and reliability of the inference.

(See 325-333)

Arguments from Analogy (pgs. 325-333)

A. In evaluating analogical arguments, students should be able to . . .

1. discern whether the compared items in an analogical argument share a sufficient number ofrelevant similarities to warrant accepting the conclusion;

2. discern whether the compared items in an analogical argument share a sufficient number ofrelevant dissimilarities to warrant rejecting the conclusion;

3. understand that with increased sample size, diversity becomes a mark of strength;

4. gauge the specificity of the conclusion relative to the premises.

B. You should understand that the same standards used to evaluate analogical arguments apply in constructing arguments from analogy .

(See 334-340)

Causal Arguments (pgs. 334-340)

A. In evaluating causal arguments, students should . . .

1. understand that it is easier to show that something could not be the cause of some effect than it is to prove a causal relationship.

2. recognize causal terms such as, produce, is responsible for, affects, makes, changes and contributesto;

3. recognize whether an argument concerns the cause of a single instance or a general relationship;

a. note that causal claims about populations usually mean that some condition or event results in a higher rate of some supposed effect in the population, not that every instance of some event will result in the supposed effect;

b. note that causal relationships are often complex, so that even a genuine causal factor may neither be necessary nor sufficient to bring about the effect under consideration;

4. understand the distorting effect of selective attention and memory to evidence supporting a causal conclusion;

5. understand the unreliability of anecdotal evidence;

6. recognize the merit of a controlled experiment in discerning causal relationships;

a. be familiar with terminology associated with controlled experiments:

1. experimental group

2. control group

3. placebo effect

4. double-blind study

5. distinguish relationships of correlation from relationships of causation ;

a. distinguish between a positive correlation and a negative correlation;

b. understand when a correlation is significant.

(See 341-347)

Students should understand the concept of probability. (pgs. 341-347) To this end, you should be able to recognize and distinguish between . . .

A. epistemic probability;

B. relative frequency probability;

C. a priori probability. In grasping the concept of a priori probability, you should be familiar with the following concepts:

1. gamblers fallacy;

2. the law of large numbers;

3. expected value;

4. relative value;

5. diminishing marginal value.





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